Modern data centers often comprise thousands of hosts that operate collectively to service requests from even larger numbers of remote clients. During operation, components of these data centers can produce significant volumes of machine-generated data. The loosely structured and unstructured nature of much of this data has made it challenging to perform indexing and searching operations because of the difficulty of applying semantic meaning to such data. As the number of hosts and clients associated with a data center continues to grow, processing large volumes of machine-generated data in an intelligent manner and effectively presenting the results of such processing continues to be a priority.
Moreover, the infrastructures of various kinds of computing resources, including computer systems, servers, storage systems, network communication devices, or any other electronic resource, of such a data center or information technology (IT) ecosystem typically have characteristics that can be measured. Measuring the characteristics of the computing resources is vital to mitigating instabilities and detecting vulnerabilities. Examples of the characteristics include temperature, utilization, availability, etc. For example, measuring the health of a datacenter's infrastructure, services, service components, backend systems, and various types of application programming interfaces (APIs) is important to enable organizations to proactively monitor, diagnose, and analyze the infrastructure, application, and business metrics of the datacenter.
The performance metrics (e.g., metrics) are useful time-series measurements of computing resources for IT operations and application management. Metrics are used to analyze performance of one or more systems in a domain. Specifically, a metric represents a performance measurement of a computing resource. The metric includes a numerical value indicative of a characteristic of the computing resource measured at a point in time. The numerical value may also be referred to as the “measure” of the metric. In some cases, a metric can represent a data point of a time series of characteristic measurements taken of a computing resource. The numerical value may be a floating point value including any number of decimal values that reflects a precision of that measurement. In some embodiments, the number can be an integer value.
Metrics can be measured at short intervals for multiple applications and/or systems, resulting in large data sets. Metrics measurements can be at the root of everything deployed and managed in at least some known IT environments. From on-premises to cloud deployments, measurements of such metrics enable analysts to understand the availability, performance, and health of mission critical services delivered to end users. Such metrics measurements can provide insights into trends and facilitate a comparison of what is normal and what is not. Existing systems for processing and analyzing metrics data remain inadequate and fail to provide meaningful insights into the health of computing resources.
Metrics can also be helpful in assessing machine-generated data generated by various components in IT environments, such as servers, sensors, routers, mobile devices, Internet of Things (IoT) devices, etc., and for business analytics and security. Analyzing large volumes of machine-generated data has become imperative to obtaining critical insights of systems and their computing resources. However, existing systems for analyzing machine-generated data are incapable of providing insights that benefit from metrics data, which is processed independently by separate systems. As such, analyzing metrics data and/or machine-generated data of computing resources is often difficult, thereby creating a significant cognitive burden on analysts to determine meaningful insights about systems.